Suspect identification based on descriptive facial attributes

We present a method for using human describable face attributes to perform face identification in criminal inves-tigations. To enable this approach, a set of 46 facial at-tributes were carefully defined with the goal of capturing all describable and persistent facial features. Using crowd sourced labor, a large corpus of face images were manually annotated with the proposed attributes. In turn, we train an automated attribute extraction algorithm to encode target repositories with the attribute information. Attribute extrac-tion is performed using localized face components to im-prove the extraction accuracy. Experiments are conducted to compare the use of attribute feature information, derived from crowd workers, to face sketch information, drawn by expert artists. In addition to removing the dependence on expert artists, the proposed method complements sketch-based face recognition by allowing investigators to imme-diately search face repositories without the time delay that is incurred due to sketch generation. 1

Video-to-video face matching: Establishing a baseline for unconstrained face recognition

Abstract Face recognition in video is becoming increasingly important due to the abundance of video data captured by surveillance cameras, mobile devices, Internet uploads, and other sources. Given the aggregate of facial information contained in a video (i.e., a sequence of face images or frames), video-based face recognition solutions can potentially alleviate classic challenges caused by variations in pose, illumination, and expression. However, with this increased focus on the development of algorithms specifically crafted for video-based face recognition, it is important to establish a baseline for the accuracy using state-of-theart still image matchers. Note that most commercial-offthe-shelf (COTS) offerings are still limited to single frame matching. In order to measure the accuracy of COTS face recognition systems on video data, we first investigate the effectiveness of multi-frame score-level fusion and analyze the consistency across three COTS face matchers. We demonstrate that all three COTS matchers individually are superior to previously published face recognition results on the unconstrained YouTube Faces database. Further, fusion of scores from the three COTS matchers achieves a 20% improvement in accuracy over previously published results. We encourage the use of these results as a competitive baseline for video-to-video face matching on the YouTube Faces database

Timeliness of contact tracing among flight passengers for influenza A/H1N1 2009

<p>Abstract</p> <p>Background</p> <p>During the initial containment phase of influenza A/H1N1 2009, close contacts of cases were traced to provide antiviral prophylaxis within 48 h after exposure and to alert them on signs of disease for early diagnosis and treatment. Passengers seated on the same row, two rows in front or behind a patient infectious for influenza, during a flight of ≥ 4 h were considered close contacts. This study evaluates the timeliness of flight-contact tracing (CT) as performed following national and international CT requests addressed to the Center of Infectious Disease Control (CIb/RIVM), and implemented by the Municipal Health Services of Schiphol Airport.</p> <p>Methods</p> <p>Elapsed days between date of flight arrival and the date passenger lists became available (contact details identified - CI) was used as proxy for timeliness of CT. In a retrospective study, dates of flight arrival, onset of illness, laboratory diagnosis, CT request and identification of contacts details through passenger lists, following CT requests to the RIVM for flights landed at Schiphol Airport were collected and analyzed.</p> <p>Results</p> <p>24 requests for CT were identified. Three of these were declined as over 4 days had elapsed since flight arrival. In 17 out of 21 requests, contact details were obtained within 7 days after arrival (81%). The average delay between arrival and CI was 3,9 days (range 2-7), mainly caused by delay in diagnosis of the index patient after arrival (2,6 days). In four flights (19%), contacts were not identified or only after > 7 days. CI involving Dutch airlines was faster than non-Dutch airlines (<it>P </it>< 0,05). Passenger locator cards did not improve timeliness of CI. In only three flights contact details were identified within 2 days after arrival.</p> <p>Conclusion</p> <p>CT for influenza A/H1N1 2009 among flight passengers was not successful for timely provision of prophylaxis. CT had little additional value for alerting passengers for disease symptoms, as this information already was provided during and after the flight. Public health authorities should take into account patient delays in seeking medical advise and laboratory confirmation in relation to maximum time to provide postexposure prophylaxis when deciding to install contact tracing measures. International standardization of CT guidelines is recommended.</p

Sex differences in money pathology in the general population

This study examined sex differences in money beliefs and behaviours. Over 100,000 British participants completed two measures online, one of which assessed “money pathology” (Forman in Mind over money, Doubleday, Toronto, 1987), and the other four “money types”, based on the emotional associations of money (Furnham et al. in Personal Individ Differ, 52:707–711, 2012). Nearly all measures showed significant sex differences with medium to large effect sizes, and with females exhibiting more “money pathology” than males. The biggest difference on the money types was on money being associated with generosity (money representing love) where men scored much lower than females, and autonomy (money representing freedom) where men scored higher than women. For men, more than women, money represented Power and Security. Men were more likely to be Hoarders while women did more emotional regulatory purchasing. Implications and limitations of this study are discussed